Optimal distributed generation allocation in unbalanced radial distribution networks via empirical discrete metaheuristi
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ORIGINAL PAPER
Optimal distributed generation allocation in unbalanced radial distribution networks via empirical discrete metaheuristic and steepest descent method Francisco Carlos Rodrigues Coelho1 · Ivo Chaves da Silva Junior2 · Bruno Henriques Dias2 Vitor Hugo Ferreira3 · André Luiz Marques Marcato2
· Wesley Peres1
·
Received: 8 April 2020 / Accepted: 3 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Loss minimization and voltage improvement through distributed generation (DG) planning is a well-established problem. However, a careful review of the literature shows that there is still room for the development of efficient algorithms for this purpose. In special, hybridization between optimization techniques is suitable for this complex problem, as it allows taking advantage of the positive features of different approaches. In this work, a novel empirical discrete metaheuristic (EDM) is presented and merged with the steepest descent method to solve the DG allocation problem. The allocation is broken into two subproblems: sitting the DGs and sizing them. The EDM deals with the first subproblem, while the second one is solved by the steepest descent method in an interchangeable optimization structure. The EDM tackles some key limitations of metaheuristic family methods. Relatively, it shows: low results variability in different executions; low initial conditions dependency; and few number parameters to tune. All simulations are performed in a communication scheme using the softwares Matlab and OpenDSS. The obtained results with IEEE 34-bus and IEEE 123-bus distribution test systems were compared to the literature and other metaheuristics, attesting the quality of the proposed approach. Keywords Empirical discrete metaheuristic (EDM) · Distributed generation allocation · Hybrid optimization · Unbalanced radial distribution system · Loss minimization φ
Abbreviations
PDG j
Optimization problem
PG j , PD j
Fobj PL φ Vj NB Ph DG j
QG j , Q Dj
B
Objective function Power losses (kW) Voltage at bus j, in phase φ (V) Set of network buses Set of phases:φ ∈ {A, B, C} Distributed generation status at bus j (on/off)
Francisco Carlos Rodrigues Coelho [email protected]
1
Department of Electrical Engineering, Federal University of São João del-Rei (UFSJ), São João del-Rei, MG, Brazil
2
Department of Electrical Energy, Federal University of Juiz de Fora (UFJF), Juiz de Fora, MG, Brazil
3
Department of Electrical Engineering, Fluminense Federal University (UFF), Niterói, RJ, Brazil
φ
φ
φ
φ
φ
φ
f P jk , f Q jk max PDG V lim
Real power injection into bus j by the DG unity, in phase φ (kW) Real power generation and demand in bus j, in phase φ (kW) Reactive power generation and demand in bus j, in phase φ (kvar) Real and reactive power flow from bus k to bus j, in phase φ (kW, kvar) Real power DG unity upper bound (kW) Voltage limit, V min or V max (V)
Empirical discrete metaheuristic N ITE G STAG t xn,g
Number of individuals/solutions Maximum number of iterat
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